Modeling and Control of E-jet Printing Process for Multi-layer Structures

Abstract

Electrohydrodynamic jet (e-jet) printing is a high-resolution additive manufacturing (AM) technique that holds promise for the fabrication of customized micro-devices. Through e-jet printing, a 3D structure is generated by sequential addition of material on the surface [1, 6, 7]. The performance of such structures depends on the uniformity and consistency of the layers [1]. Depending on the application, in order to have high performance and yield, the fabrication process must be able to provide strict adherence to the desired thickness and spatial resolutions. The lack of real-time monitoring methods that can capture, analyze and react to in situ measurements has been a challenge for most AM systems, and in particular µ-AM systems, in which the key dynamics occur at the micro-/nanoscales. Most AM processes run in open-loop and system parameters are tuned by human operators through trial and error. AM processes are innately iteration varying and system parameters change from layer to layer. Control methods that leverage the iterative nature of AM-processes are needed. In this dissertation, e-jet printing is investigated for its capability in depositing multi-layered thin-films of multiple materials with microscale spatial resolution and nanoscale thickness resolution. The traditional method to close the loop in these processes should be feedback control. However, feedback control requires realtime measurement at the microscale, which is not possible with µ−scale AM processes such as e-jet printing, in which, the millisecond time scale and micro to nano-scale length scale make the online measurements difficult. We leverage spatial iterative learning control (SILC) and model predictive control (MPC) to enable robust and intelligent controllers to autonomously direct material addition at the microscale. This dissertation focuses on modeling material deposition at the microscale, and controlling the deposition process to fabricate thin-film multi-layered and multi-material structures. First, the material interactions at the microscale are investigated to derive better models of the spatial interactions within the 3D printing, which led to the development of an empirical model of the deposition process that relates process and material parameters to the thickness and uniformity of the layers in multi-layered structures. Then, spatial iterative learning control (SILC) is used to regulate and automate material deposition at the microscale to improve the performance and reliability of the AM processes, without requiring a human operator to physically be in the environment to heuristically tune the process parameters. The proposed SILC framework addresses the combined challenges of incorporating multiple dynamic models into the framework due to the interactions driven by the different build materials and addressing iteration varying initial conditions due to the roughness of previous surfaces by leveraging iteration-to-iteration and layer-to-layer learning with the ability to correct the errors of previous layers. Additionally, this dissertation focuses on extending the SILC framework through the integration of model predictive control (MPC) in order to impose input constraints associated with AM processes and improve robustness and performance of the additive process. The feasibility of the proposed spatial control frameworks to direct the deposition process at the microscale are demonstrated through the experimental validations and simulation case studies using a model of an electrohydrodynamic jet (e-jet) printing process.